Unsupervised Learning - Analyze feature relevance, feature distribution, scatter matrix, heatmap, feature scaling, detect and remove outliers, PCA, dimensionality reduction, Gaussian Mixture Model clustering, data recovery by inverse transformations
Computer Vision: 3D based reconstruction of foregrounds in the video onther test with manual calibration of 4 cameras.
Identify speaker from given speech signal using MFCC features and Gaussian Mixture Models
Gravitational-wave population inference allowing narrow population features with minimal edge bias
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